Informatics has changed dramatically in the past 10 years. The changes in health care alone have forced organizations to do things far differently than they once imagined.
Scenario
You are a member of a planning committee in your organization whose purpose is to research and report on future trends in the field of informatics. You will look at future trends in informatics that can improve your organization’s health care strategy and day-to-day patient care. In addition, you will consider how the technology is designed and whether your co-workers or patients will be willing and able to use it. You are preparing a report that will be delivered to your committee’s sponsoring executive.
Identify the stakeholders in your organization that you would be presenting to. This is the first step in what will ultimately be a formal 5-year plan proposal for technology investments and resource allocation, which will be delivered to your board of directors for approval.
Create a report that outlines the 3 hottest trends that are forecast for informatics. Use data to support your conclusion. This will provide you with timely knowledge of what is happening in the field.
Answer the following questions in your report:
How would your selected trends affect patient outcomes?
How can these technologies be used to address specific challenges facing the health care industry? For example, AI can be used to develop personalized treatment plans.
What are the steps that need to be taken to implement these technologies? This could include discussing the costs, risks, and challenges associated with implementation.
Provide examples of how each trend is already being used to improve health care. This could include examples from other hospitals, health systems, or even other industries.
Informatics Future Trends Report
This report outlines the three most transformative trends in informatics, supported by growth data, and details their impact on healthcare strategy and patient care.
1. Artificial Intelligence (AI) and Machine Learning (ML)
AI/ML is the use of algorithms to analyze vast datasets to make predictions or assist in decision-making. The global AI in healthcare market, valued at approximately $14 billion in 2020, is projected to reach over $119.8 billion by 2027 (CAGR of $\approx 35.9\%$).
Impact on Patient Outcomes
Improved Diagnosis: AI analyzes medical imaging (MRI, X-ray) and pathology slides faster and more accurately than the human eye, enabling earlier detection of cancers, neurological conditions, and retinal diseases.
Personalized Treatment: ML models analyze a patient's genomics, Electronic Health Record (EHR) data, and past treatment outcomes to suggest tailored therapies that are more likely to be effective.
Addressing Healthcare Challenges
Staff Burnout and Administrative Waste: Generative AI automates non-clinical tasks such as drafting referral letters, summarizing patient histories, and automating medical coding for billing. This reduces the administrative burden on clinicians, freeing them to focus on direct patient care.
Predictive Risk Management: AI leverages real-time data to predict high-risk events like sepsis, heart failure, or hospital readmission before they occur, allowing clinicians to intervene proactively.
Implementation Steps, Costs, and Risks
Steps: Establish a data governance framework to ensure data quality. Invest in cloud infrastructure to handle large training datasets. Acquire or develop a core AI model and integrate it with the existing EHR system (Clinical Decision Support).
Costs: High upfront cost for specialized AI/ML talent, cloud computing infrastructure, and model training.
Risks/Challenges: Data bias (AI trained on non-diverse data can perpetuate health inequities). Lack of Explainability (the "black box" problem where clinicians may distrust recommendations they can't fully trace). Regulatory hurdles (FDA clearance for clinical use).
Current Use Examples
Clinical Decision Support: IBM Watson Health has been a pioneer, using ML to provide clinical decision support by analyzing large clinical datasets to match physician recommendations.
Radiology: Hospitals use AI algorithms to automatically flag suspicious regions in CT scans or mammograms, boosting the radiologist's efficiency and reducing missed diagnoses.
2. Telehealth and Remote Patient Monitoring (RPM)
This trend leverages digital communication and connected devices (Internet of Medical Things - IoMT) to deliver care outside of traditional settings.
Impact on Patient Outcomes
Proactive Chronic Disease Management: RPM uses wearable sensors and devices (e.g., blood pressure cuffs, glucose meters) to collect patient vital signs in real-time. This allows for early intervention when abnormalities are detected, leading to better management of conditions like heart failure and diabetes, and a reduction in emergency hospital visits.
Increased Access and Adherence: Telehealth consultations remove geographic and mobility barriers, making care accessible to patients in rural/underserved areas and increasing medication/treatment plan adherence through frequent, convenient check-ins.
Addressing Healthcare Challenges
Geographic Inequality: Telehealth directly addresses the challenge of care access in rural areas by facilitating virtual consultations, including teletherapy for mental health services where local resources are scarce.